Most industrial systems are automated in order to operate efficiently. Monitoring the state of the system in real-time is essential for smooth functioning of automated systems. This monitoring function can be done manually, or multiple sensors may be employed to record reading about the state of the system at various instances of time, which results in a very large amount of data. These sensor readings or manually monitored readings are analyzed to detect anomalies in the system. At present, the data analysis is carried out either manually or semi-automatically. An anomaly is detected by using a probability model, three sigma models, regression models, time series models, covariance matrix and QR decomposition method. But there are limitations of using these models. The existing methods use only statistical methods to detect anomalies, which may report large number of false positives. The existing methods are developed mostly considering real value sensor reading and not for other data types (e.g. categorical).